1. Field of the Invention
The present invention relates to a vehicle state quantity predictor and method for predicting state quantities representing the movement of a vehicle. More specifically, the present invention relates to a vehicle state quantity predictor and method for predicting a state quantity of a vehicle that models the movement of a vehicle to calculate a state quantity thereof to predict the actual state quantity of the vehicle. The present invention also relates to a vehicle steering controller and method using a vehicle state quantity predictor and method of predicting a vehicle state quantity.
2. Description of the Related Art
An existing vehicle driving system has been described in which a vehicle is driven on a pre-established road surface adapted for use with automatic steering control system. In the described vehicle driving system, markers (magnetic markers) are installed in the road surface at prescribed intervals along the path of travel of the vehicle. Each time the vehicle passes a marker, a detection signal is output from a marker sensor mounted on board the vehicle. The detection signal indicates the relative positional relationship between the marker and the vehicle and is used as the basis for detecting the lateral displacement of the vehicle in the path of vehicle travel. The vehicle is automatically steered to avoid deviating from the path of vehicle travel, based on the lateral displacement detected as the vehicle passes each marker.
In the described existing vehicle driving system, in order to achieve greater steering control accuracy, the use of Kalman filters to predict state quantities representing the yaw and lateral translation of the vehicle required for steering control (for example, as described in Japanese Patent Application Publication No. JP-A-2001-34341) has been proposed. JP-A-2001-34341 describes using the Kalman filter to calculated the predicted values of four state quantities (yaw rate, yaw angle, lateral displacement speed, and lateral displacement) defined as state quantities representing the yaw and the lateral translation of the vehicle, using the observed value of the lateral displacement obtained each time the vehicle passes a marker.
In JP-A-2001-34341 a distance-domain Kalman filter that acquires the observed lateral displacement and observed yaw rate each time the vehicle passes a magnetic marker and predicts the values of state quantities of the vehicle using the observed values, and a time-domain Kalman filter that acquires the observed yaw raw rate at prescribed time intervals and that uses the predicted value calculated the previous time to calculate the predicted values of the state quantities of the vehicle using the observed values. Each time the distance-domain Kalman filter acquires a predicted amount, the time-domain Kalman filter uses the value predicted by the distance-domain Kalman filter in place of the predicted value from the previous time to calculate the predicted value of the state quantities of the vehicle. That is, by causing the predicted values of the vehicle acquired by the distance-domain Kalman filter for each magnetic marker to be reflected in the prediction by the time-domain Kalman filter, the accuracy of predicting the state quantities of the vehicle is improved, even between markers at which the lateral displacement cannot be observed.
However, JP-A-2001-34341 indicates that because until the next magnetic marker is passed, the predicted value obtained by the distance-domain Kalman filter at the immediately previous passing of a magnetic marker is reflected in the prediction by the time-domain Kalman filter, if there is a large lateral translation of the vehicle between magnetic markers, there is a tendency for the prediction error of state quantities between the magnetic marker to become large. That is, because lateral translation cannot be observed between magnetic markers, errors caused by the construction of the actual road surface, yaw rate drift, and modeling of the vehicle tend to accumulate. The result is that, in the case in which the period of having the predicted value obtained by the distance-domain Kalman filter reflected in the time-domain Kalman filter becomes long (for example, when traveling at a very slow speed along a curve with a high curvature rate), the error in prediction by the time-domain Kalman filter during magnetic marker tends to increase.